Workflow

Detailed software versions can be found under Rules.

Results

File Size Description Job properties
RGI_heatmap-4.png 94.1 kB

AMR genes categorised by Drug Class and samples have been clustered hierarchically (see SciPy documentation). Yellow represents a perfect hit, teal represents a strict hit, purple represents no hit. Genes with asterisks (*) appear multiple times because they belong to more than one Drug Class category in the antibiotic resistance ontology (ARO).
Rulesummary_results
File Size Description Job properties
B9_GI.gff3 16 Bytes
Rulegenomic_island_prediction
Wildcardssample=B9
C7_GI.gff3 16 Bytes
Rulegenomic_island_prediction
Wildcardssample=C7
D2_GI.gff3 16 Bytes
Rulegenomic_island_prediction
Wildcardssample=D2
DMW1-1_GI.gff3 85 Bytes
Rulegenomic_island_prediction
Wildcardssample=DMW1-1
File Size Description Job properties
B9.tsv.html 104 Bytes
Ruleplasmid_prediction
Wildcardssample=B9
C7.tsv.html 313 Bytes
Ruleplasmid_prediction
Wildcardssample=C7
D2.tsv.html 104 Bytes
Ruleplasmid_prediction
Wildcardssample=D2
DMW1-1.tsv.html 526 Bytes
Ruleplasmid_prediction
Wildcardssample=DMW1-1
plasmid_summary.tsv.html 200 Bytes

Plasmid prediction with ABRICATE using PlasmidFinder Database
Rulesummary_results
File Size Description Job properties
B9.tsv.html 6.0 kB
Rulevirulence_identification
Wildcardssample=B9
C7.tsv.html 104 Bytes
Rulevirulence_identification
Wildcardssample=C7
D2.tsv.html 4.4 kB
Rulevirulence_identification
Wildcardssample=D2
DMW1-1.tsv.html 104 Bytes
Rulevirulence_identification
Wildcardssample=DMW1-1
virulence_summary.tsv.html 693 Bytes

Virulence Factor genes predicted with ABRICATE using VFDB database
Rulesummary_results

Statistics

If the workflow has been executed in cluster/cloud, runtimes include the waiting time in the queue.

Rules

Rule Jobs Output Singularity Conda environment Code
summary_results 1
  • WGCA_analysis_result/RGI_heatmap
  • WGCA_analysis_result/virulence_summary.tsv
  • WGCA_analysis_result/plasmid_summary.tsv
  • WGCA_analysis_result/RGI_heatmap-4.png
 
source
summarize_plasmid_prediction 1
  • plasmid_prediction/summary.tsv
  • abricate =0.8.13
  • perl-list-moreutils
 
1
abricate --summary {input} > {output}
summarize_virulence 1
  • virulence_genes/summary.tsv
  • abricate =0.8.13
  • perl-list-moreutils
 
1
abricate --summary {input} > {output}
generate_resistome_heatmap 1
  • resistome_summary/RGI_heatmap
 
source
genomic_island_prediction 4
  • genomic_islands/C7_GI.gff3
  • genomic_islands/D2_GI.gff3
  • genomic_islands/B9_GI.gff3
  • genomic_islands/DMW1-1_GI.gff3
  • islandpath =1.0.4
 
1
islandpath {input} {output}
plasmid_prediction 4
  • plasmid_prediction/C7.tsv
  • plasmid_prediction/D2.tsv
  • plasmid_prediction/B9.tsv
  • plasmid_prediction/DMW1-1.tsv
  • abricate =0.8.13
  • perl-list-moreutils
 
1
abricate --minid=60 --mincov=90 --db=plasmidfinder {input} > {output}
virulence_identification 4
  • virulence_genes/C7.tsv
  • virulence_genes/D2.tsv
  • virulence_genes/B9.tsv
  • virulence_genes/DMW1-1.tsv
  • abricate =0.8.13
  • perl-list-moreutils
 
1
abricate --minid=90 --mincov=60 --db=vfdb {input} > {output}
resistome_prediction 4
  • resistome_prediction/C7
  • resistome_prediction/D2
  • resistome_prediction/B9
  • resistome_prediction/DMW1-1
 
1
docker run -v $PWD:/data -t quay.io/biocontainers/rgi:4.2.2--py35ha92aebf_1 rgi main -i data/{input} -o data/resistome_prediction/{wildcards.sample} -t contig --clean --debug > {output.log}
genome_annotation 4
  • genome_annotation/C7.gbk
  • genome_annotation/D2.gbk
  • genome_annotation/B9.gbk
  • genome_annotation/DMW1-1.gbk
  • prokka
 
1
prokka --force --outdir genome_annotation/ --usegenus --Genus Enterococcus --prefix {wildcards.sample} {input}